ETL Versus ELT | Explained With Examples

When it comes to data engineering, there are two common approaches for transforming and loading data. The first approach is ETL(Extract Transform Load). The other is referred to as ELT (Extract, Load, Transform). One easily notices that the transform and load steps in the acronym, TL vs LT, are swapped. However, this small swap in wording has much larger implications in data processing. To explain this, let’s start with a quick explanation of ETL.

ETL stands for Extract, Transform, and Load. ETL is a high-level data processing and loading architecture. The first step of ETL is Extract, which refers to the process of extracting data from a source. This can be through hitting an API to receive the data, picking up the data from SFTP or S3, or downloading the data from a URI. The next step is Transform, which are the data transformations to the data to conform it, clean it, and aggregate it to its final stage. This can involve modifying the data format, converting JSON to CSV, or other modifications such as joining the dataset to other data sources. Finally, the last step is load, which refer to loading the fully transformed data into a database.

Simplified Example- ETL of Data for Ice Cream Stores

In our simplified example, we will build an ETL process for pulling and gathering data for an Ice Cream store. We will need to hit an API to extract data about ice cream stores. We need to roll the data up to a store level and store the final result in our database.

Source Data from API

[
 {
  transaction_id:"1"
  store:"10",
  date:"05/01/2020 10:05:01"
  price:100.5
 },
 {
  transaction_id:"2"
  store:"10",
  date:"05/01/2020 10:06:02"
  price:120.5
 },
]

#database #data #data-engineering #data-science

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ETL Versus ELT | Explained With Examples

Anil Cynix

1592470812

Before we learn anything about ETL Testing its important to learn about Business Intelligence and Dataware. Let’s get started –

What is BI?
Business Intelligence is the process of collecting raw data or business data and turning it into information that is useful and more meaningful. The raw data is the records of the daily transaction of an organization such as interactions with customers, administration of finance, and management of employee and so on. These data’s will be used for “Reporting, Analysis, Data mining, Data quality and Interpretation, Predictive Analysis”.

What is Data Warehouse?
A data warehouse is a database that is designed for query and analysis rather than for transaction processing. The data warehouse is constructed by integrating the data from multiple heterogeneous sources.It enables the company or organization to consolidate data from several sources and separates analysis workload from transaction workload. Data is turned into high quality information to meet all enterprise reporting requirements for all levels of users.

What is ETL?
ETL stands for Extract-Transform-Load and it is a process of how data is loaded from the source system to the data warehouse. Data is extracted from an OLTP database, transformed to match the data warehouse schema and loaded into the data warehouse database. Many data warehouses also incorporate data from non-OLTP systems such as text files, legacy systems and spreadsheets.

Let see how it works

For example, there is a retail store which has different departments like sales, marketing, logistics etc. Each of them is handling the customer information independently, and the way they store that data is quite different. The sales department have stored it by customer’s name, while marketing department by customer id.

Now if they want to check the history of the customer and want to know what the different products he/she bought owing to different marketing campaigns; it would be very tedious.

The solution is to use a Datawarehouse to store information from different sources in a uniform structure using ETL. ETL can transform dissimilar data sets into an unified structure.Later use BI tools to derive meaningful insights and reports from this data.

The following diagram gives you the ROAD MAP of the ETL process

ETL Testing or Datawarehouse Testing : Ultimate Guide

1.Extract

  • Extract relevant data
    2.Transform
  • Transform data to DW (Data Warehouse) format
  • Build keys - A key is one or more data attributes that uniquely identify an entity. Various types of keys are primary key, alternate key, foreign key, composite key, surrogate key. The datawarehouse owns these keys and never allows any other entity to assign them.
  • Cleansing of data :After the data is extracted, it will move into the next phase, of cleaning and conforming of data. Cleaning does the omission in the data as well as identifying and fixing the errors. Conforming means resolving the conflicts between those data’s that is incompatible, so that they can be used in an enterprise data warehouse. In addition to these, this system creates meta-data that is used to diagnose source system problems and improves data quality.

3.Load

  • Load data into DW ( Data Warehouse)
  • Build aggregates - Creating an aggregate is summarizing and storing data which is available in fact table in order to improve the performance of end-user queries.

What is ETL Testing?
ETL testing is done to ensure that the data that has been loaded from a source to the destination after business transformation is accurate. It also involves the verification of data at various middle stages that are being used between source and destination. ETL stands for Extract-Transform-Load.

ETL Testing Process
Similar to other Testing Process, ETL also go through different phases. The different phases of ETL testing process is as follows

ETL Testing or Datawarehouse Testing : Ultimate Guide

ETL testing is performed in five stages

1.Identifying data sources and requirements
2.Data acquisition
3.Implement business logics and dimensional Modelling
4.Build and populate data
5.Build Reports
ETL Testing or Datawarehouse Testing : Ultimate Guide.

These are the basics of etl testing course if u want to know more plz visit Online IT Guru website.

#etl testing #etl testing course #etl testing online #etl testing training #online etl testing training #etl testing online training

 iOS App Dev

iOS App Dev

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Optimizing dbt and Google’s BigQuery

Dbt makes it possible for data engineers to develop a workflow, write data transformation rules, and deploy the entire data modeling process.

Setting up a data warehouse is the first step towards fully utilizing big data analysis. Still, it is one of many that need to be taken before you can generate value from the data you gather. An important step in that chain of the process is data modeling and transformation. This is where data is extracted, transformed, and loaded (ETL) or extracted, loaded, and transformed (ELT). For this article, we’ll use the term ETL synonymously.

ETL as a process is not always as straightforward as it seems. On top of that, there are real challenges to overcome in setting up streamlined data modeling processes, including dealing with relations between data points and simplifying the queries to make the whole set of processes scalable. Even with Google offering a lot of features through its BigQuery, some degree of optimization is still required.

This is where a data build tool or dbt becomes a crucial tool. Dbt is a data transformation framework that empowers data engineers through simple SQL commands. Dbt makes it possible for data engineers to develop a workflow, write data transformation rules, and deploy the entire data modeling process.

#performance #data #etl #data warehouse #data engineering #data transformation #elt #etl testing #etl solution #data warehousing analytics

Ray  Patel

Ray Patel

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Private Constructor in Java: Use Cases Explained with Example

Introduction

A private constructor in Java is used in restricting object creation. It is a special instance constructor used in static member-only classes. If a constructor is declared as private, then its objects are only accessible from within the declared class. You cannot access its objects from outside the constructor class.

Private Constructor Use-Cases

Private constructors in Java are accessed only from within the class. You cannot access a private constructor from any other class. If the object is yet not initialised, then you can write a public function to call the private instructor. If the object is already initialised, then you can only return the instance of that object. A private constructor in Java has the following use-cases:

  • You can use it with static members-only classes.
  • You can use it with static utility or constant classes.
  • You can use it to serve singleton classes.
  • You can use it to assign a name, for instance, creation by utilising factory methods.
  • You can use it to prevent subclassing.

Singleton Class

The private constructor in Java is used to create a singleton class. A singleton class is a class in Java that limits the number of objects of the declared class to one. A private constructor in Java ensures that only one object is created at a time. It restricts the class instances within the declared class so that no class instance can be created outside the declared class. You can use the singleton class in networking and database connectivity concepts.

#full stack development #java #private constructor #private constructor java #private constructor in java: use cases explained with example #use cases explained with example

Uriah  Dietrich

Uriah Dietrich

1618457700

What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

  • ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
  • ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data

Mikel  Okuneva

Mikel Okuneva

1596834000

Which Tools Do You Use in ETL Testing?

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While there are several advanced ETL Testing tools accessible for software testing -tools, software testing companies specifically use “Informatica Data Validation”. Informatica Data Validation is one of the famous ETL tool, which integrates with the PowerCenter Repository & Integration Services. This advanced tool allows business analysts and developers to create rules to test the mapped data.

While distinct approaches to ETL testing are error-prone, very time-consuming, and seldom provide total test coverage. Informatica Data Validation Option gives an ETL testing tool that can speed up and automate ETL testing in both production environments and development & test, which means that you can deliver repeatable, complete, and auditable test coverage in minimum time with no programming skills required. Automated ETL Testing reduces time consumption and helps to maintain accuracy.

Key Features of Informatica Data Validation (ETL Testing tool):

  • Informatica Data Validation gives the complete result for data validation along with data integrity
  • Recognizes and prevents data issues and gives greater business productivity
  • Minimizes programming efforts on account of the intuitive user interface and built-in operators
  • It has Wizards to generate test Query without including the user’s requirement to write SQL
  • More sophisticated tests tool also proffers design Library & reusable Query Snippets
  • Analyze millions of data rows & columns in minutes
  • Assists to compare data from source files & data stores to the target Data Warehouse
  • Produce informative reports, updates, and auto-email outcome.
  • 50 to 90% of efforts and costs can be saved using the Informatica Data Validation tool.

Informatica Data Validation”. Informatica Data Validation

Automating ETL tests permits everyday testing without any user intervention and also aids to supports automatic regression tests on the old code following every single new release. In the end, this sophisticated tool will save your precious time and your users will appreciate the quality of your Business Intelligence deliverable.

#etl-testing #software-engineering #etl #software-testing #etl-tool